class_docstr: Bridge class to connect langchain.embeddings module to mindsDB
class_docstr_tok: ["Bridge","class","to","connect","langchain.embeddings","module","to","mindsDB"]
docstr:         Convert a row in the input dataframe into a document
  
          Default implementation is to concatenate all the columns
          in the form of
          field1: value1
  field2: value2
  ...
          
docstr_tok: ["Convert","a","row","in","the","input","dataframe","into","a","document","Default","implementation","is","to","concatenate","all","the","columns","in","the","form","of","field1",":","value1","field2",":","value2","..."]
code: def row_to_document(self, row: pd.Series) -> str:
      """
      Convert a row in the input dataframe into a document
  
      Default implementation is to concatenate all the columns
      in the form of
      field1: value1\nfield2: value2\n...
      """
      fields = row.index.tolist()
      values = row.values.tolist()
      document = "\n".join(
          [f"{field}: {value}" for field, value in zip(fields, values)]
      )
      return document
  
file_path: mindsdb/mindsdb/integrations/handlers/langchain_embedding_handler/langchain_embedding_handler.py
imports: import copy
  import importlib
  from typing import Dict, Union
  import pandas
  from langchain.embeddings.base import Embeddings
  from pandas import DataFrame
  from mindsdb.integrations.libs.base import BaseMLEngine
  from mindsdb.utilities import log
label_desc: Use image node_id 4 for calling the LangchainEmbeddingHandler obj's underlying member method code with example usage: obj.row_to_document(row) and returns: document